How to use c_test_1 method in Lemoncheesecake

Best Python code snippet using lemoncheesecake

NaiveBayesKde.py

Source:NaiveBayesKde.py

1import numpy as np2import matplotlib.pyplot as plt3from sklearn.neighbors import KernelDensity4from sklearn.model_selection import StratifiedKFold5class NaiveBayesKde:6    def __init__(self):7        self.best_bw = 08        self.best_valid_err = 19        self._trained = False10        self._errors = []11    def _kde_estimate(self, fit_x, train_x, valid_x, bw):12        kde = KernelDensity(bandwidth=bw).fit(fit_x)13        return kde.score_samples(train_x), kde.score_samples(valid_x)14    def _kde(self, fit_x, test_x, bw):15        kde = KernelDensity(bandwidth=bw).fit(fit_x)16        return kde.score_samples(test_x)17    def _kde_calc_fold(self, Xs, Ys, tr_ix, va_ix, bw):18        x_c0 = Xs[tr_ix[Ys[tr_ix] == 0]]19        x_c1 = Xs[tr_ix[Ys[tr_ix] == 1]]20        # probability of the classes21        c_train_0 = c_valid_0 = np.log(len(tr_ix[Ys[tr_ix] == 0]) / len(tr_ix))22        c_train_1 = c_valid_1 = np.log(len(tr_ix[Ys[tr_ix] == 1]) / len(tr_ix))23        for feat in range(0, Xs.shape[1]):24            # calculate logarithmic density for class 0, i.e log(P(x_i | c_0))25            t_dens, v_dens = self._kde_estimate(x_c0[:,[feat]], Xs[tr_ix][:,[feat]], Xs[va_ix][:,[feat]], bw)26            c_train_0 += t_dens27            c_valid_0 += v_dens28            # calculate logarithmic density for class 1, i.e log(P(x_i | c_1))29            t_dens, v_dens = self._kde_estimate(x_c1[:,[feat]], Xs[tr_ix][:,[feat]], Xs[va_ix][:,[feat]], bw)30            c_train_1 += t_dens31            c_valid_1 += v_dens32        # calculate predictions33        predict_train = np.argmax([c_train_0, c_train_1], axis=0)34        predict_valid = np.argmax([c_valid_0, c_valid_1], axis=0)35        # compare the predictions with our data sets36        cmp_train = Ys[tr_ix] == predict_train37        cmp_valid = Ys[va_ix] == predict_valid38        # obtain training error and validation error39        train_err = (len(tr_ix) - np.sum(cmp_train)) / len(tr_ix)40        valid_err = (len(va_ix) - np.sum(cmp_valid)) / len(va_ix) 41        return train_err, valid_err42    def optimize_bandwidth(self, X_r, Y_r, folds, start_bw, end_bw, step_bw):43        kfold = StratifiedKFold(n_splits=folds)44        best_bw = 045        best_valid_err = 146        for bw in np.arange(start_bw, end_bw + step_bw, step_bw):47            train_err = valid_err = 048            for tr_ix, va_ix in kfold.split(Y_r, Y_r):49                tr_err, va_err = self._kde_calc_fold(X_r, Y_r, tr_ix, va_ix, bw)50                train_err += tr_err / folds51                valid_err += va_err / folds52            self._errors.append([bw, train_err, valid_err])53            if (valid_err < best_valid_err):54                best_bw = bw55                best_valid_err = valid_err56        self.best_bw = best_bw57        self.best_valid_err = best_valid_err58        return self.best_bw, self.best_valid_err59    def fit(self, X_r, Y_r):60        if self.best_bw == 0:61            raise Exception('The bandwidth should be optimized first')62        self._p_c0 = np.log(np.sum(Y_r == 0) / len(Y_r))63        self._p_c1 = np.log(np.sum(Y_r == 1) / len(Y_r))64        self._x_c0 = X_r[Y_r == 0]65        self._x_c1 = X_r[Y_r == 1]66        self._trained = True67        68    def predict(self, X_t, bw):69        if self._trained == False:70            raise Exception('The classifier is not trained')71        c_test_0 = self._p_c072        c_test_1 = self._p_c173        for feat in range(0, X_t.shape[1]):74            test_dens = self._kde(self._x_c0[:,[feat]], X_t[:,[feat]], bw)75            c_test_0 += test_dens76            test_dens = self._kde(self._x_c1[:,[feat]], X_t[:,[feat]], bw)77            c_test_1 += test_dens78        predictions = np.argmax([c_test_0, c_test_1], axis=0)79        return predictions80    def plot_errors(self, save_fig=True, fig_name='NB.png'):81        errors = np.array(self._errors)82        plt.figure()83        plt.title('Training vs Cross Validation Errors')84        plt.plot(errors[:,0], errors[:,1], 'b', label='Training Error')85        plt.plot(errors[:,0], errors[:,2], 'r', label='Cross-Validation Error')86        plt.legend()87        88        if save_fig:89            plt.savefig(fig_name)90        plt.show()...

nankomah_Project2.py

Source:nankomah_Project2.py

1#!/usr/bin/env python32# -*- coding: utf-8 -*-3# import os4# os.chdir('./Project2/')5import numpy as np6from cvxopt import matrix7from cvxopt.solvers import qp8import matplotlib.pyplot as plt9def generate_vars(n=100, m = 30):10    """[returns B- nxn, A - mxn, c - mx1]11    Args:12        n (int, optional): [number of columns]. Defaults to 100.13        m (int, optional): [number of rows]. Defaults to 30.14    Returns:15        [np arrays]: [Generated Matrix and Vector]16    """    17    sigma = 1 / (4*n)18    I = np.eye(n)19    K = np.random.normal(scale=sigma, size=(n,n))20    21    A = np.random.uniform(size=(m,n))22    B = I + K + K.T23    c = np.random.uniform(size=(m))24    return A, B, c25def cvxpot_qp(A,c,B):26    """[return x_star]27    Args:28        A ([np.array]): [A - mxn]29        c ([np.array]): [c - mx1]30        B ([np.array]): [B - nxn]31    Returns:32        [type]: [x_star]33    """    34    m = A.shape[0]35    n = A.shape[1]36    q = matrix(np.zeros((n,1)))37    P = matrix(B)38    A = matrix(A)39    b = matrix(c)40    cvx_results= qp(P = P, q = q, A=A, b=b)41    return np.asarray(cvx_results['x']).flatten()42def lagrangian_x(A,c,B,c_k,lam):43    """[returns x_k based on d_x alm solve for x   ]44    Args:45        A ([np.array]): [A - mxn]46        c ([np.array]): [c - mx1]47        B ([np.array]): [B - nxn]48        c_k ([float]): [variable will the following condition49         must be > 0, -> infty, c_{i} <= c_{i+1} ]50        lam ([np.array]): [lam - mx1]51    Returns:52        [x]: [mx1 np.array]53    """    54    leftside = 2*B+(c_k*A.T@A)55    rightside = (c_k*A.T@c) - (A.T@lam)56    x = np.linalg.pinv(leftside)@rightside57    return x.flatten()58def constraint(A,x,c):59    """[returns output of constraint]60    Args:61        A ([np.array]): [A - mxn]62        x ([np.array]): [x - mx1]63        c ([np.array]): [c - mx1]64    Returns:65        [type]: [mx1]66    """    67    return (A@x) - c 68def alm(B,A,c, epsilon=10**(-10)):69    """[uses Augmented Lagrangian method to find an x_k simlar to x_star]70    Args:71        B ([np.array]): [B - nxn]72        A ([np.array]): [A - mxn]73        c ([np.array]): [c - mx1]74        epsilon ([float], optional): [threshold value]. Defaults to 10**(-13).75    Returns:76        [list]: [list of relative error ]77    """    78    x_star = np.asarray(cvxpot_qp(A,c,B*2)).flatten()79    error_i = [] 80    lambda_k = np.zeros(c.shape)81    #c_k must be > 0, -> infinity, c_k_{i} <= c_k_{i+1} 82    c_k =.183    i = 0 84    while True:85        x_k = lagrangian_x(A,c,B,c_k,lambda_k)86        error = np.linalg.norm(x_k - x_star) /  np.linalg.norm(x_star)87        error_i.append(error)88        stopping = np.linalg.norm(A@x_k - c)89        if stopping <= epsilon:90            return x_k, i, error_i91        92        lambda_k += c_k*constraint(A,x_k,c)93        c_k = 2.0**i94        i += 195def main():96    """[main function]97    """    98    #a)99    A = np.loadtxt('A_test_1.txt')100    c = np.loadtxt('c_test_1.txt')101    B = np.loadtxt('B_test_1.txt')102    # Q, A, b = generate_vars()103    #b)104    x_star = cvxpot_qp(A,c,B*2)105    print('cvx output')106    print(x_star)107    #c) 108    x_k, i, error_i = alm(B,A,c)109    print()110    print('Augmented Lagrangian method output')111    print(x_k)112    print()113    print('Final Error')114    print(error_i[-1])115    print()116    print('Final Error is below 10^(-6)')117    print(error_i[-1] <= 10**(-6))118    print()119    120    fig = plt.figure()121    plt.title('Relative Error')122    plt.plot(np.arange(len(error_i)), error_i, label = 'Relative Error vs k')123    plt.legend()124    plt.grid(True)125    fig.tight_layout()126    # plt.savefig('pix/Given_sample.png', dpi=fig.dpi)127    plt.show()128if __name__ == "__main__":...

B.py

Source:B.py

...5}6@lcc.suite("BB1")7class BB1:8    @lcc.test("Test of BB1")9    def c_test_1(self):10        import time11        time.sleep(0.21)12        require_that("value", 1, is_integer(2))13@lcc.suite("BB2")14class BB2:15    @lcc.test("Test of BB2")16    def d_test_1(self):...

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